The progression of human breast cancer is a complex multivariate problem that requires several approaches for adequate address. Invasive breast carcinoma cells upregulate Mena, a member of the Ena/VASP family, versus cells within the original primary tumor. Further, the expression of an alternatively spliced Mena isoform, MenalNV, confers sensitivity to epidermal growth factor (EGF) and mediates lung metastasis. The mechanisms underlying these Mena-mediated behaviors is unknown. We hypothesize that the aberrant expression of the Mena isoforms that confer hypersensitivity to EGF results in quantifiable changes in motility-related signaling network activities involving multiple pathway components, with respect to both spatial and temporal dynamics. The objective of this study is to combine experiment and computation to determine how differential Mena isoform expression rewires the EGF-stimulated signaling network leading to breast cancer cell invasion. The specific aims of the study are (1) Quantify the effect of Mena isoform overexpression on EGF-mediated human breast tumor line motility behaviors, (2) Determine the effect of the invasive Mena isoforms on differential binding of known and novel Mena interacting partners, and (3) Develop a computational model relating EGF-mediated signaling pathway activities to cell motility behavior in the context of differential Mena isoform expression. We will generate stable transfectants of four Mena isoforms in non-invasive human breast cancer cell lines and study their EGF-stimulated cell motility in vitro. Next, the metastatic potential of Mena expressing MCF-7 cells will be determined using a xenograft model. Concurrently, we determine if the inclusion of additional structural elements within the alternatively spliced exons result in alteration of Mena binding partners. These experiments will lend insight to the mechanism by which Mena isoforms promote EGF sensitivity. Finally, we will construct a data-driven, multivariate statistical model to determine how Mena isoform expression alters the network leading to cell invasion. The model will be tested by prediction of motility responses after perturbation to the most important signaling variables identified by the modeling framework. In the long term, the multivariate model can be used to predict network responses to pharmacologic intervention and will lend important insight to the effects of drugs that target individual, and perhaps more importantly, multiple points within the motility pathway. Our approach provides the possibility of identifying non-trivial compensatory mechanisms which may mediate drug resistance.